Robust Visual Tracking via Semiadaptive Weighted Convolutional Features

In recent years, hierarchical features extracted from convolutional neural network (CNN) for robust visual tracking have been developed by several methods. As features from different layers characterize different information of target and are set fixed weighted parameters, the performance of traditional visual tracking methods based on CNN can be further improved. In this letter, we propose a novel online visual tracking method by using hierarchical convolutional features with semiadaptive weights. The responses from different layers are assessed by a novel loss function. It considers the log likelihood and the entropy term of each response. The layer with the lower loss value is set to a higher weight parameter and the layer with the higher loss value is set to a lower one. We further develop a target appearance pyramid to deal with the scale change and an online classifier to redetect targets in case of tracking failures. Extensive experiments on challenging videos demonstrate that our method can achieve better tracking results in terms of lower center location error and higher overlap rate.